基于Factoid嵌入的无监督用户身份链接
Unsupervised User Identity Linkage via Factoid Embedding
摘要：
用户身份链接（UIL）是一个跨多个在线社交网络（OSN）的用户帐户匹配问题，是一个被广泛研究的问题，对许多现实世界的应用非常重要。大多数现有的UIL解决方案采用监督或半监督的方法，通常遭受标记数据的稀缺性。在本文中，我们提出了Factoid Embedding，这是一个采用无监督方法的新框架。它的目的是应付不同的配置文件属性，内容类型和不同的OSN的网络链接。其关键思想是，关于用户身份的每一条信息都描述了真实的身份所有者，从而将所有者与其他用户区分开来。我们用一个factoid来表示这样一条信息，并将其建模为一个由用户身份、谓词和对象或另一个用户身份组成的三元组。通过嵌入这些factoid，我们学习用户身份的潜在表示和链接两个用户身份从不同的OSN，如果他们是彼此接近的用户嵌入空间。我们的模拟仿真嵌入算法的设计是这样的，当我们学习嵌入空间时，每个嵌入的模拟仿真都被“翻译”成用户嵌入空间中的运动，以使相似的用户身份更接近，不同的用户身份更远离。进行了大量的实验，以评估两个真实世界的OSN数据集上的仿真嵌入。实验结果表明，即使在没有训练数据的情况下，Factoid Embedding的性能也优于现有的方法。
英文摘要：
User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Most existing UIL solutions adopt a supervised or semi-supervised approach which generally suffer from scarcity of labeled data. In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach. It is designed to cope with different profile attributes, content types and network links of different OSNs. The key idea is that each piece of information about a user identity describes the real identity owner, and thus distinguishes the owner from other users. We represent such a piece of information by a factoid and model it as a triplet consisting of user identity, predicate, and an object or another user identity. By embedding these factoids, we learn the user identity latent representations and link two user identities from different OSNs if they are close to each other in the user embedding space. Our Factoid Embedding algorithm is designed such that as we learn the embedding space, each embedded factoid is "translated" into a motion in the user embedding space to bring similar user identities closer, and different user identities further apart. Extensive experiments are conducted to evaluate Factoid Embedding on two real-world OSNs data sets. The experiment results show that Factoid Embedding outperforms the state-of-the-art methods even without training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
